Quantum Multi-Agent Reinforcement Learning for Autonomous Mobility Cooperation

2023/08/03 by Soohyun Park, Park, Soohyun, Jae Pyoung Kim +7 · 3 citations
Computer Science · Neuroscience · Physics and Astronomy · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Multiagent Systems (cs.MA) #Neural Networks and Reservoir Computing #Neural dynamics and brain function #stochastic dynamics and bifurcation

paper · pdf · doi:10.48550/arxiv.2308.01519

openalex publication_date 2023/08/03 · openalex created_date 2023/08/19 · openalex updated_date 2026/07/28

Abstract

For Industry 4.0 Revolution, cooperative autonomous mobility systems are widely used based on multi-agent reinforcement learning (MARL). However, the MARL-based algorithms suffer from huge parameter utilization and convergence difficulties with many agents. To tackle these problems, a quantum MARL (QMARL) algorithm based on the concept of actor-critic network is proposed, which is beneficial in terms of scalability, to deal with the limitations in the noisy intermediate-scale quantum (NISQ) era. Additionally, our QMARL is also beneficial in terms of efficient parameter utilization and fast convergence due to quantum supremacy. Note that the reward in our QMARL is defined as task precision over computation time in multiple agents, thus, multi-agent cooperation can be realized. For further improvement, an additional technique for scalability is proposed, which is called projection value measure (PVM). Based on PVM, our proposed QMARL can achieve the highest reward, by reducing the action dimension into a logarithmic-scale. Finally, we can conclude that our proposed QMARL with PVM outperforms the other algorithms in terms of efficient parameter utilization, fast convergence, and scalability.

Cited by

• Quantum Reinforcement Learning for 6G and Beyond Wireless Networks
• Quantum Reinforcement Learning: Recent Advances and Future Directions
• How Can Quantum Deep Learning Improve Large Language Models?

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